Method and system for a range reduction scheme for user selection in a multiuser MIMO downlink transmission

ABSTRACT

Various aspects of a method and a system for a range reduction scheme for user selection in a multiuser MIMO downlink transmission are presented. Aspects of a system for range reduction may comprise a range reduction processor that determines a plurality of channel measurements corresponding to a plurality of signals. The range reduction processor may compute a plurality of channel capacities based on the channel measurements corresponding to a subset of the plurality of signals having channel gain that is greater than a remaining portion of the plurality of signals. Aspects of a method may comprise determining a plurality of channel measurements corresponding to a plurality of signals, and computing a plurality of channel capacities based on said channel measurements corresponding to a subset of the plurality of signals having a channel gain that is greater than a remaining portion of the plurality of signals.

CROSS-REFERENCE TO RELATED APPLICATIONS/INCORPORATION BY REFERENCE

This application makes reference to:

U.S. application Ser. No. 11/232,340 filed Sep. 21, 2005;

U.S. application Ser. No. 11/232,266 filed Sep. 21, 2005;

U.S. application Ser. No. 11/231,501 filed Sep. 21, 2005;

U.S. application Ser. No. 11/231,586 filed Sep. 21, 2005;

U.S. application Ser. No. 11/232,369 filed Sep. 21, 2005;

U.S. application Ser. No. 11/231,701 filed Sep. 21, 2005;

U.S. application Ser. No. 11/232,362 filed Sep. 21, 2005;

U.S. application Ser. No. 11/231,557 filed Sep. 21, 2005; and

U.S. application Ser. No. 11/231,416 filed Sep. 21, 2005.

Each of the above stated applications is hereby incorporated byreference in its entirety.

FIELD OF THE INVENTION

Certain embodiments of the invention relate to processing of signals incommunications systems. More specifically, certain embodiments of theinvention relate to a method and system for a range reduction scheme foruser selection in a multiuser multiple-input-multiple-output (MIMO)downlink transmission.

BACKGROUND OF THE INVENTION

Mobile communications have changed the way people communicate and mobilephones have been transformed from a luxury item to an essential part ofevery day life. The use of mobile phones is today dictated by socialsituations, rather than hampered by location or technology. While voiceconnections fulfill the basic need to communicate, and mobile voiceconnections continue to filter even further into the fabric of every daylife, the mobile Internet is the next step in the mobile communicationrevolution. The mobile Internet is poised to become a common source ofeveryday information, and easy, versatile mobile access to this datawill be taken for granted.

Third generation (3G) cellular networks have been specifically designedto fulfill these future demands of the mobile Internet. As theseservices grow in popularity and usage, factors such as cost efficientoptimization of network capacity and quality of service (QoS) willbecome even more essential to cellular operators than it is today. Thesefactors may be achieved with careful network planning and operation,improvements in transmission methods, and advances in receivertechniques. To this end, carriers need technologies that will allow themto increase downlink throughput and, in turn, offer advanced QoScapabilities and speeds that rival those delivered by cable modem and/orDSL service providers.

In order to meet these demands, communication systems using multipleantennas at both the transmitter and the receiver have recently receivedincreased attention due to their promise of providing significantcapacity increase in a wireless fading environment. These multi-antennaconfigurations, also known as smart antenna techniques, may be utilizedto mitigate the negative effects of multipath and/or signal interferenceon signal reception. It is anticipated that smart antenna techniques maybe increasingly utilized both in connection with the deployment of basestation infrastructure and mobile subscriber units in cellular systemsto address the increasing capacity demands being placed on thosesystems. These demands arise, in part, from a shift underway fromcurrent voice-based services to next-generation wireless multimediaservices that provide voice, video, and data communication.

The utilization of multiple transmit and/or receive antennas is designedto introduce a diversity gain and to raise the degrees of freedom tosuppress interference generated within the signal reception process.Diversity gains improve system performance by increasing receivedsignal-to-noise ratio and stabilizing the transmission link. On theother hand, more degrees of freedom allow multiple simultaneoustransmissions by providing more robustness against signal interference,and/or by permitting greater frequency reuse for higher capacity. Incommunication systems that incorporate multi-antenna receivers, a set ofM receive antennas may be utilized to null the effect of (M−1)interferers, for example. Accordingly, N signals may be simultaneouslytransmitted in the same bandwidth using N transmit antennas, with thetransmitted signal then being separated into N respective signals by wayof a set of N antennas deployed at the receiver. Systems that utilizemultiple transmit and receive antennas may be referred to asmultiple-input multiple-output (MIMO) systems. One attractive aspect ofmulti-antenna systems, in particular MIMO systems, is the significantincrease in system capacity that may be achieved by utilizing thesetransmission configurations. For a fixed overall transmitted power, thecapacity offered by a MIMO configuration may scale with the increasedsignal-to-noise ratio (SNR). For example, in the case of fadingmultipath channels, a MIMO configuration may increase system capacity bynearly M additional bits/cycle for each 3-dB increase in SNR.

The widespread deployment of multi-antenna systems in wirelesscommunications has been limited by the increased cost that results fromincreased size, complexity, and power consumption. This poses problemsfor wireless system designs and applications. As a result, some initialwork on multiple antenna systems may be focused on systems that supportsingle user point-to-point links. However, the use of multi-antennatechniques for a multiuser environment to improve total throughputremains a challenge.

Further limitations and disadvantages of conventional and traditionalapproaches will become apparent to one of skill in the art, throughcomparison of such systems with some aspects of the present invention asset forth in the remainder of the present application with reference tothe drawings.

BRIEF SUMMARY OF THE INVENTION

A system and/or method is provided for a range reduction scheme for userselection in a multiuser multiple-input-multiple-output (MIMO) downlinktransmission, substantially as shown in and/or described in connectionwith at least one of the figures, as set forth more completely in theclaims.

These and other features and advantages of the present invention may beappreciated from a review of the following detailed description of thepresent invention, along with the accompanying figures in which likereference numerals refer to like parts throughout.

BRIEF DESCRIPTION OF SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an exemplary multi-user basestation in a multiuser environment, which may be utilized in connectionwith an embodiment of the invention.

FIG. 2 is a block diagram illustrating an exemplary MIMO base stationutilizing range reduction, in accordance with an embodiment of theinvention.

FIG. 3 is a histogram representation of indexes for optimal userscompiled from 1000 random channel realizations, which may be utilized inconnection with an embodiment of the invention.

FIG. 4 is an empirical cumulative distribution function (CDF), which maybe utilized in connection with an embodiment of the invention.

FIG. 5 is a flow chart that illustrates exemplary steps in a method fora range reduction scheme for user selection in a multiuser MIMO downlinktransmission, in accordance with an embodiment of the invention.

FIG. 6 is a chart illustrating exemplary capacity performance associatedwith various downlink transmission schemes, in accordance with anembodiment of the invention.

FIG. 7 is a chart illustrating exemplary bit error rate (BER)performance associated with various downlink transmission schemes, inaccordance with an embodiment of the invention.

DETAILED DESCRIPTION OF THE INVENTION

Certain embodiments of the invention may be found in a system and/ormethod for a range reduction scheme for user selection in a multiusermultiple-input-multiple-output (MIMO) downlink transmission. Variousembodiments of the invention may reduce the computational complexity, ata base station comprising multiple antennas, associated with selecting asubset of mobile terminals which are to simultaneously receive a signalfrom the base station. The mobile terminals in the subset may beselected to maximize the capacity of information that is transmittedfrom the base station during a given time interval. Those signals in thesubset may be referred to as being in the reduced range.

Communication systems using multiple antennas at both the transmitterand the receiver have received increased attention due to their promiseof providing significant capacity increases in a wireless fadingenvironment. However, many of the pioneering approaches to multipleantenna systems have been restricted to single user point-to-pointlinks. Recently, attention has been focused on improvements in totalthroughput that may be realized by utilizing multi-antenna techniques ina multiuser environment. A communication from a base station to a mobileterminal, or user, may be referred to as a downlink communication. Withmultiple antennas installed, the base station may select a group of theusers (active users) to transmit their respective data streamssimultaneously. The information transfer rate that may be achieved in adownlink communication between the base station in a multiuserenvironment, comprising a plurality of users, may be equal to a sum ofthe downlink information transfer rates of the active users. The maximumsum of the downlink transfer rates may be referred to as the sumcapacity associated with a plurality of users. A communication from auser to a base station may be referred to as an uplink communication.

In a multiuser communication system, employing multiple antennas at thebase station may improve the downlink system capacity. The capacityimprovement may be attained by communicating simultaneously withmultiple users by utilizing precoding at the transmitter when channelstate information is available (CSI). A zero-forcing (ZF) linearprecoder may achieve a sum capacity when the number of users, K,approaches infinity. The sum capacity may be based on the number oftransmitting antennas that are located at the base station. Furthermore,ZF precoders may provide near-optimal performance even with a limitednumber of users, for example when K=10. Therefore, preceding combinedwith user selection in a multiuser environment may represent a promisingtechnique that may be utilized in broadband wireless data communicationsystems.

FIG. 1 is a block diagram illustrating an exemplary multi-user basestation in a multiuser environment, which may be utilized in connectionwith an embodiment of the invention. Referring to FIG. 1, there is showna base station 102, a first user 104, a second user 106, a signal 112and a signal 114. The base station 902 may comprise a plurality ofantennas 102 a, 102 b, 102 c, and 102 d. The base station 102 mayutilize antennas 102 a-102 d to transmit a signal 112. At the same time,the base station 102 may utilize antennas 102 a-102 d to transmit asignal 114. The signal 112 may comprise information that is intended tobe transmitted from the base station 102 to the first user 104. Thesignal 114 may comprise information that is intended to be transmittedfrom the base station 102 to the second user 106. The transmittedsignals 112 and 114 may be received by the first user 104, and by thesecond user 106. At the first user 104, the signal 114 may represent aninterference signal. At the second user 106, the signal 112 mayrepresent an interference signal. The interference signal may be reducedby utilizing beamforming at the base station 102.

FIG. 2 is a block diagram illustrating an exemplary MIMO base stationutilizing range reduction, in accordance with an embodiment of theinvention. Referring to FIG. 2, there is shown a base station 200 and aplurality of users 240 . . . 250. The base station 200 may comprise aplurality of channel encoders 202 . . . 204, a range reduction block206, a user scheduler 208, a channel station information block 210, aplurality of modulators 212 . . . 214, a processor 216, system memory218, a power control block 220, a beamforming or linear preceding block222, a plurality of antennas 224 . . . 226. The power control block maycomprise a plurality of multipliers 220 a . . . 220 b. The user 240 maycomprise an antenna 228, a demodulator 230, and a channel decoder 232.The user 250 may comprise an antenna 234, a demodulator 236, and achannel decoder 238.

The channel encoders 202 . . . 204 may comprise suitable logic,circuitry, and/or code that may be adapted to encode binary dataassociated with as many as K individual information streams. The rangereduction block 206 may comprise suitable logic, circuitry, and/or codethat may be adapted to selecting a subset comprising L users from alarger set of K users. For example, the range reduction block 206 mayselect 10 users, L=10, from a larger group of 100, K=100, users. The 10users may comprise the reduced range. The user scheduler 208 maycomprise suitable logic, circuitry, and/or code that may be adapted toselect a group of M or less than M users that are to receive one of M orless than M signals simultaneously transmitted via an antenna 224 . . .226. The selected users may be from a set of L users as selected by therange reduction block 206. For example, the user scheduler 208 mayselect a group of 2 users, M=2, for a subset of 10, L=10 users. Thegroup of 2 users may be selected based on a criterion that maximizes theinstantaneous rate of information transfer from the base station 200.The channel state information (CSI) block 210 may comprise suitablelogic, circuitry, and/or code that may be adapted to provide channelstate information of a plurality of users 240 . . . 250. The channelstate information of a plurality of users 240 . . . 250 may be obtainedby uplink channel estimation in a time division duplex (TDD) system orby CSI feedback received from the users through a dedicated feedbacklink in a frequency division duplex (FDD) system.

The modulators 212 . . . 214 may comprise suitable logic, circuitry,and/or code that may be adapted to modulate the binary data of each ofthe users selected by the user scheduler 208. In this regard, themodulation operation on the binary data may result in a plurality ofcomplex symbols u₁ . . . u_(M), for example. The power control block 220may comprise suitable logic, circuitry, and/or code that may be adaptedto allocate different power levels, p₁ . . . p_(M), to complex symbolsu₁ . . . u_(M) received from the modulation blocks 212 . . . 214, forexample. The power level associated with each of the signals receivedfrom a modulator 212 . . . 214 u₁ . . . u_(M) may be scaled by amultiplier block 220 a . . . 220 b. The scaling may produce a pluralityof power level adjusted user data symbols p₁u₁ . . . p_(M)u_(M). Each ofthe power level adjusted user data symbols p_(i)u_(i), where i is aninteger whose value is between 1 and M inclusive, may be referred to asa spatial stream.

The beamforming or linear precoding block 222 may comprise suitablelogic, circuitry, and/or code that may be adapted to process spatialstreams and to separate signals intended for different users such thateach of the users 240 receives little or no interference from signalsintended for other users 250. With M antennas at the base station 200,the beamforming or linear precoding block 222 may separate up to Mdifferent signals, which the base station 200 may transmitsimultaneously via the antennas 224 . . . 226. The signal transmittedvia antenna 224 may comprise signal contributions from the plurality ofspatial streams. For example, the signal transmitted via antenna 224,x₁, may be represented as x₁=a₁p₁u₁+ . . . +m₁p_(M)u_(M). The constantsa₁ and m₁ represent weighting factors utilized by the beamforming orlinear preceding block 222 when forming the signal transmitted by theantenna 224. The signal transmitted via antenna 226, x_(M), may berepresented as x_(M)=a_(M)p₁u₁+ . . . +m_(M)p_(M)u_(M), for example. Theconstants a_(M) and m_(M) represent weighting factors utilized by thebeamforming or linear preceding block 222 when forming the signaltransmitted by the antenna 226. The beamforming or linear precedingblock may be referred to as a precoder.

The processor 216 may comprise suitable logic, circuitry, and/or codethat may be adapted to process information and/or data associated withthe generation of transmission signals at the base station 200. Theprocessor 216 may, for example, determine the values of parameters thatcontrol the operation of the range reduction block 206. For example, theprocessor 216 may determine a threshold value that is utilized by therange reduction block 206 to select users 240 . . . 250 to be includedin a user group. The memory 218 may comprise suitable logic, circuitry,and/or code that may be utilized to store data and/or controlinformation that may be utilized in the operation of at least a portionof the base station 218. For example, the memory 218 may be utilized tostore information that identifies users 240 . . . 250 that have beenincluded in a user group.

The user scheduler 208, the power control block 220, and/or thebeamforming or linear precoding block 222 may require knowledge of thestate of the downlink channel. The CSI block 210 may be adapted totransfer the channel state information to the user scheduler 208, thepower control block 220, and/or the beamforming or linear precedingblock 222. This does not exclude other functional blocks within the basestation 200, for example the range reduction algorithm block 206 and/orthe processor 216, from utilizing channel state information.

The user 240 may receive at least a portion of the signals transmittedby the base station 200 via the antenna 228. The demodulator 230 maycomprise suitable logic, circuitry, and/or code that may be adapted todemodulate the signals received from the base station 200, for example.The channel decoders 232 may comprise suitable logic, circuitry, and/orcode that may be adapted to decode the demodulated signals from thedemodulators 230 into binary bit streams, for example. The user 240 mayreceive at least a portion of the signals transmitted by the basestation 200 via the antenna 234. The demodulator 236 may comprisesuitable logic, circuitry, and/or code that may be adapted to demodulatethe signals received from the base station 200, for example. The channeldecoders 238 may comprise suitable logic, circuitry, and/or code thatmay be adapted to decode the demodulated signals from the demodulators236 into binary bit streams, for example.

In operation, the base station 200 comprising M antennas 224 . . . 226,may transmit a signal to each of a plurality of M users 240 . . . 250simultaneously. A selected plurality of M users 240 . . . 250 mayrepresent a group of users, comprising a subset, selected from aplurality of K total users. The sum capacity associated with a firstselected plurality of M users may be greater than the sum capacityassociated with a second selected plurality of M users, for example. Inthis case, the first selected plurality of M users may be considered tobe a better group in comparison to the second selected plurality of Musers in terms of sum rate. When the total number of users K is largecompared to the number M, the number of potential groups of users, orsubsets, may be large. Each subset may comprise a unique combination ofM users selected from the total number of K users. An optimal group mayrefer to a single M-user combination which provides the largest sum rateamong all the possible combination of M users selected from the totalnumber of K users. To find the optimal group of users to which the basestation 200 may simultaneously transmit signals, the precoder 222 maysearch all potential subsets, among the total number of K users, to findthe one with the largest instantaneous sum rate. The search range, inthis case, may be K. The complexity of the search algorithm may increasedramatically with increasing values of K.

Various embodiments of the invention may comprise a range reductionblock 206, which implements an algorithm that decides a reduced userrange L within which the user scheduler 208 searches for the best usergroup. Specifically, the user scheduler 208 may only search among the Lstrongest users for the user combination that provides the largest sumrate. The criteria for evaluating the strength of any of the K users mayutilize a signal gain measurement, wherein a first user 240 may beconsidered stronger than a second user 250 when the signal gainassociated with the first user 240 is greater than the signal gainassociated with the second user 250. The channel measurement and/orsignal gain information may be based on information retrieved from theCSI block 210. The search among the L strongest users may reduce thesearch range from a value K to a value L. When the value L is much lessthan the value K, a significant reduction in the search range may beachieved in various embodiments of the invention. The reduction in thesearch range may similarly result in a significant reduction in thecomputational complexity associated with the user scheduler 208.Furthermore, in frequency division duplex (FDD) systems in which thebase station may obtain the CSI from the users via a feedback link, theamount of feedback information received and/or processed at the basestation may be significantly reduced because the base station mayrequire full channel measurement information associated with only theplurality of L strongest users, instead of larger the plurality of Kusers. Various embodiments of the invention may comprise a method and asystem for determining a value for the reduced range L.

At the base station 200, a plurality of signals x₁ . . . x_(M) may betransmitted by a corresponding plurality of antenna 224 . . . 226. Acorresponding plurality of signals y₁ . . . y_(K) may be received byeach of a corresponding subset of K users. Each of the received signalsy₁ . . . y_(M) may comprise a contribution from a plurality oftransmitted signals x₁ . . . x_(M). For example, the signal received bya first user 240, y₁, may be represented y₁=h₁₁x₁+ . . . +h_(1M)x_(M).The plurality of transmitted signals x₁ . . . x_(M) and received signalsy₁ . . . y_(M) as shown in FIG. 2 may be expressed in a signal modelutilizing matrix notation. The signal model may be specified using thefollowing expression:

$\begin{matrix}{{\begin{bmatrix}y_{1} \\y_{2} \\\vdots \\y_{k}\end{bmatrix} = {{\begin{bmatrix}h_{1} \\h_{2} \\\vdots \\h_{k}\end{bmatrix}x} + n}},} & (1)\end{matrix}$where y_(k) (k=1, . . . , K) may represent the received signal by userk,h_(k)εC^(1×M) may represent the channel vector to user k, xεC^(1×M)may represent the transmitted symbol vector by the base station, andnεC^(k×1) may represent additive white Gaussian noise (AWGN). The AWGNmay be characterized as comprising a zero mean value and unit variance.The transmitted symbols may satisfy a power constraint, E[x^(H)x]≦P,where (•)^(H) may represent a complex conjugate transpose of the matrix(•).

Each element in h_(κ) may represent a zero-mean circularly symmetriccomplex Gaussian (ZMCSCG) random variable with unit variance. Signalsreceived by each of the users 240 may experience independent fading,hence the plurality of channel vectors

{h_(k)}_(k = 1)^(K)may each be statistically independent. The channel state information(CSI), h_(κ), may be assumed to be perfectly known to user k, but not toother users. In a time division duplex (TDD) system, the base station200 can estimate CSI associated with an uplink channel and utilize theuplink CSI to estimate CSI associated with the corresponding downlinkchannel based on a channel reciprocity property between the uplink anddownlink channels. Therefore, the base station 200 may compute channelknowledge about the CSI as observed by each of the users 240 throughaccurate channel estimation at the base station 200 derived from uplinkCSI. In frequency division duplex (FDD) systems, the base station 200may obtain CSI from the users via a rate constraint feedback link.

In multiuser communication systems, employing multiple antennas at thebase station 200 may improve downlink system capacity. The capacityimprovement may be attained by communicating simultaneously withmultiple users 240 . . . 250, utilizing precoding at the transmitterwhen channel state information (CSI) is available. Dirty paper coding(DPC) is a precoding scheme that may achieve a sum capacity, but thecomplexity of DPC may limit the ability to implement systems utilizingthe precoding scheme. There may also exist other suboptimal, butrelatively low complexity schemes for multiuser MIMO downlinktransmission, such as linear precoding, Tomlinson-Harashima preceding(THP), and vector encoding, for example.

A zero-forcing (ZF) linear precoder may achieve the sum capacity whencombined with infinite-order multiuser diversity when, for example, thenumber of users K approaches infinity. Furthermore, ZF precoders mayprovide near-optimal performance even when the number of users is notinfinite, for example when K=10. One aspect of the invention may referto the utilization of zero-forcing precoders in a multiuser environment.Various embodiments of the invention may not be limited to ZF precoders,however. Embodiments of the invention may also be applied to a pluralityof preceding schemes that utilize multiuser diversity.

Zero-forcing precoders are a specific type of linear precoders. When thebase station 200 selects a group of users to which a signal x may betransmitted, wherein the group of users may comprise D⊂{1, . . . , K}with d=|D|≦K, a linear precoding scheme may linearly weigh the datasymbols, s=[s₁, . . . , s_(d)]^(T), before they are transmitted from thebase station according to the following expression:x=FPs  (2)where x may represent the transmitted signal vector as in (1), F=[f₁, .. . , f_(d)] may represent the M×d linear precoding matrix withnormalized columns (∥f_(k)∥=1), and P=diag{P₁, . . . , P_(d)} with

${\sum\limits_{i = 1}^{d}P_{i}} \leq P$may represent the power control matrix that allocates transmit power todifferent users. The data symbols s may correspond to the data symbolsu₁ . . . u_(M) that are generated by the plurality of modulators 212 . .. 214. The elements in the linear precoding matrix F may represent theplurality of weighing coefficients utilized by the precoder 222. Thenonzero elements in the diagonal matrix P may represent the plurality ofscaling factors p₁ . . . p_(M) utilized by the power control block 220.The plurality of received signals y may be represented as in thefollowing expression:

$\begin{matrix}{\begin{bmatrix}y_{1} \\y_{2} \\\vdots \\y_{d}\end{bmatrix} = {{\begin{bmatrix}h_{1} \\h_{2} \\\vdots \\h_{d}\end{bmatrix}{FPs}} + {n.}}} & (3)\end{matrix}$

A zero-forcing precoder 222, may utilize a pseudo-inverse of a channelmatrix

H_(D) = [h₁^(T), …  , h_(d)^(T)]^(T)as a weighting matrix when H_(D) has full row rank, for example, when:

$\begin{matrix}{{W_{D} = {H_{D}^{\dagger} = {H_{D}^{H}\left( {H_{D}H_{D}^{H}} \right)}^{- 1}}},} & (4) \\{{F_{D} = {W_{D}\begin{bmatrix}\frac{1}{w_{1}} & \; & \; \\\; & \ddots & \; \\\; & \; & \frac{1}{w_{d}}\end{bmatrix}}},} & (5)\end{matrix}$where

{w_(i)}_(i = 1)^(d)are the column of W_(D)

By defining:

$\begin{matrix}{\xi_{i}\overset{\Delta}{=}\frac{1}{w_{i}}} & (6)\end{matrix}$and substituting (5) to (3), an equation representing the receivedsignal may be obtained for each user when zero-forcing precoding isutilized as in the following expression:y _(i)=ξ_(i) P _(i) s _(i) +n _(i), ∀iεD.  (7)

Thus, the multiuser downlink channel may be represented as a set ofparallel channels. The maximum sum rate for a given user group D may berepresented as in the following expression:

$\begin{matrix}{{C_{D} = {\sum\limits_{i \in D}{\log\left( {1 + {\xi_{i}P_{i}}} \right)}}},} & (8)\end{matrix}$where the sum C_(D) in (8) may represent a sum rate associated with asingle group of M users 240 selected from a range of K users. Theoptimal value P_(i) may be determined base on a water-filling solutionas in the following expression:

$\begin{matrix}{{P_{i} = \left( {\mu - \frac{1}{\xi_{i}}} \right)^{+}},} & (9)\end{matrix}$where the water level μ may be selected to satisfy the condition

${\sum\limits_{i \in D}\left( {\mu - \frac{1}{\xi_{i}}} \right)^{+}} = {P.}$The maximum achievable sum rate for a given channel realization may thusbe obtained by searching over all the possible user groups arerepresented as in the following expression:

$\begin{matrix}{C = {\max\limits_{{D \subseteq {\{{1,\mspace{11mu}\ldots\mspace{11mu},K}\}}},{{D} \leq M}}{C_{D}.}}} & (10)\end{matrix}$where C in (10) may represent a maximum value of C_(D) derived from aplurality of groups of M users selected from the reduced range of Kusers. A channel realization may comprise a selected set of channels,for example M, by which the base station 200 may simultaneously transmitinformation to selected users within a user group.

As shown in (10), for a given channel realization, the optimalbrute-force user group selection for ZF preceding may require searchingover all

$\sum\limits_{i = 1}^{M}\begin{pmatrix}K \\i\end{pmatrix}$possible user groups to find the one with the largest sum rate, whichmay lead to a high computational cost, when K is large.

However, it is not always necessary to search among all the K users toidentify optimal user groups. Various embodiments of the inventionutilize a reduced search block 206 that may reduce the search size froma value of K to a smaller value L by reducing the range of the search tocomprise only the L users with the strongest channel gains. This mayreduce the complexity of the search to

$\sum\limits_{i = 1}^{M}{\begin{pmatrix}L \\i\end{pmatrix}.}$When L<<K, the benefit in terms of reduction in computational complexitymay be significant.

An example of a conventional approach to optimal group selection may berepresented by a system with M=2 antennas 224, 226 at a base station 200and K=100 users each equipped with a single antenna 228. For eachchannel realization

{h_(k)}_(k = 1)^(K),the users may be sorted and indexed in terms of their channel gain as inthe following expression:γ₁≧γ₂≧ . . . ≧γ_(κ)  (11)where

$\gamma_{k}\overset{\Delta}{=}{h_{k}}^{2}$may represent a value of signal gain, or channel gain, associated withsignals transmitted by the base station 200 and received by a user k. Inorder to maximize the sum rate, the base station 200 may select anoptimal user group according to (10).

FIG. 3 is a histogram representation of indexes for optimal userscompiled from 1000 random channel realizations, which may be utilized inconnection with an embodiment of the invention. Referring to FIG. 3,there is shown a histogram 302. The histogram 302 comprises a pluralityof user indexes, k, and represents a number of instances in which theuser, identified by the corresponding user index, may be selected in achannel realization. For example, the user indicated by user index=1 maybe selected as an optimal user, for inclusion in a user group, in over700 of the 1,000 channel realizations.

FIG. 4 is an empirical cumulative distribution function (CDF), which maybe utilized in connection with an embodiment of the invention. Referringto FIG. 4, there is shown a cumulative distribution function (CDF) 402.The CDF comprises a plurality of user indexes, k, and represents theprobability that the user, identified by the corresponding index k, andeach of the preceding users, identified by preceding correspondingindexes to the index k, may be selected in a channel realization. Forexample, the first 10 users, indicated by user indexes that are lessthan or equal to 10, may be selected as optimal users in approximately95% of channel realizations. The selected 10 users may represent the 10strongest users.

One aspect of the invention may exploit the likelihood that a subset ofthe strongest users 240 may be included in a large percentage channelrealizations. This realization may indicate that an optimum user group,associated with a maximum sum rate, may be determined, with a highdegree of statistical likelihood, based on a reduced range search thatincludes only a subset of total users. By limiting the range of thesearch for the best user groups to only the L=10 strongest users, thenumber of possible combinations of user groups searched may be reducedfrom

${\sum\limits_{i = 1}^{M}\begin{pmatrix}K \\i\end{pmatrix}} = 5050$ to ${{\sum\limits_{i = 1}^{M}\begin{pmatrix}L \\i\end{pmatrix}} = 55},$corresponding to a complexity reduction of about 100, for example.

FIG. 5 is a flow chart that illustrates exemplary steps in a method fora range reduction scheme for user selection in a multiuser MIMO downlinktransmission, in accordance with an embodiment of the invention.Referring to FIG. 5, in step 502, channel state information (CSI) may bederived, step 504 comprise a search for optimal users, step 506 maycomprise computing a cumulative distribution function (CDF) among userindexes, and step 508 may comprise computing a reduced search range.

In step 502 CSI may be derived based on a plurality of T independentchannel realizations, for example:{h _(κ)(t)}_(κ=1) ^(κ), t=1, . . . , T.  (12)

The CSI may comprise channel gain, or signal gain, information. For eachchannel realization, users among the full set of K users may be sorted,and indexed, in an order based on the values of the channel gainscorresponding to each of the K users. For example, a user with a largervalue of corresponding channel gain may be placed in the sorted list ata higher index than a user with a smaller value of corresponding channelgain as in the following expression:γ₁(t)≧γ₂(t)≧γ_(κ)(t), t=1, . . . , T,  (13)where

${\gamma_{\kappa}(t)}\overset{\Delta}{=}{{{h_{\kappa}(t)}}^{2}.}$

The channel measurement may be carried out either by offline channelsounding or by online channel estimation. In a time division duplex(TDD) system, the base station may compute a channel estimate associatedwith an uplink channel, and use the uplink channel estimation as anapproximation of channel estimates for the corresponding downlinkchannel based on a channel reciprocity property between the uplink anddownlink channels. In a frequency division duplex (FDD) system, thereciprocity property may not be utilized. Thus, the downlink CSI may beestimated by the users 240 . . . 250 and subsequently communicated tothe base station 200 via a dedicated feedback link.

In step 504, for each of the channel realizations according to (12), theoptimal user group may be determined according to (8) and (10) as in thefollowing expression:

$\begin{matrix}{{{D_{opt}(t)} = {\arg{\max\limits_{{D \subseteq {\{{1,\mspace{11mu}\ldots\mspace{11mu},K}\}}},{{D} \leq M}}{C_{D}(t)}}}},{t = 1},\ldots\mspace{11mu},T,{where}} & (14) \\{{{C_{D}(t)} = {\sum\limits_{i \in D}{\log\left( {1 + {{\xi_{i}(t)}{P_{i}(t)}}} \right)}}},} & (15)\end{matrix}$and where ξ_(i)(t) and P_(i)(t) may be as defined in (6) and (9),respectively. D_(opt)(t) may be represented as a row vector thatcontains indexes corresponding to the users 240 . . . 250 contained inthe optimal group for channel realization t. By representing the indexof the optimal users as a random variable X the vector as in thefollowing expression:

$\begin{matrix}{D_{opt}\overset{\bigtriangleup}{=}\left\lbrack {{D_{opt}(1)},{D_{opt}(2)},\ldots\mspace{14mu},{D_{opt}(T)}} \right\rbrack} & (16)\end{matrix}$may contain samples of the random variable X.

In step 506, an estimate of the cumulative distribution function (CDF){circumflex over (F)}(X) of X may be produced based on samples from theoptimal user index vector, X, that was determined in step 504 accordingto (16).

In step 508, a threshold, δ_(th)ε(0,1], may be selected. The reducedsearch range then be determined by the relationship as in the followingexpression:L={circumflex over (F)} ⁻¹(δ_(th)),  (17)where {circumflex over (F)}⁻¹(•), is the inverse function of {circumflexover (F)}(•), for example:X={circumflex over (F)} ⁻¹({circumflex over (F)}(X)).The threshold may be a measure of the likelihood that the a channelrealization, evaluated among the full range of K users, will comprisethe subset of L users.

In various embodiments of the invention, expression (17) may beimplemented by tabulating the CDF {circumflex over (F)}(X) in terms ofthe random variable comprising the index of optimal users X, andsearching for a value of X that corresponds to δ_(th) The thresholdδ_(th) may provide a measure of the statistical likelihood that the sumrate, computed among of subset of L users in the reduced searchingrange, may approach the optimal performance computed among the fullgroup of K users.

While the exemplary embodiment of the invention illustrates a searchrange reduction scheme a system that utilizes a simple zero-forcingprecoder, the invention is not so limited. Various embodiments of theinvention may also be utilized with other more sophisticated precoders,for example a minimum mean squared error (MMSE) precoder, aTomlinson-Harashima precoding (THP) precoder, or a sphere encodingprecoder, for example.

FIG. 6 is a chart illustrating exemplary capacity performance associatedwith various downlink transmission schemes, in accordance with anembodiment of the invention. The chart may be based on exemplaryconditions in which the total number of users K=100 and the number oftransmitting antenna at the base station M=2. Each user 240 may comprisea single receiving antenna 228. The channels for each channelrealization may be generated to be flat Rayleigh faded channels. Thetransmit antennas 224 . . . 226 at the base station 200 may be placed ata sufficient distance that individual channels may experience fadingthat is independent of the fading in other channels. The modulationformat utilized may be quaternary phase shift keying (QPSK).

Referring to FIG. 6, there is shown a graph illustrating the capacityperformance of a downlink scheme in which the reduced range value L=100,602, a graph illustrating the capacity performance of a downlink schemein which the reduced range value L=10, 604, a graph illustrating thecapacity performance of a downlink scheme in which the reduced rangevalue L=5, 606, and a graph illustrating the capacity performance of adownlink scheme in which the reduced range value L=2, 608. In the graph602, the reduced range may be equal to the full range, that is, thegraph may illustrate the full search capacity performance. In the graph604, the reduced search may search a subset comprising only the 10strongest users. The capacity performance associated with the reducedrange L=10 graph 604 may be substantially equal to the capacityperformance of the full search graph 602.

The capacity performance comparison may represent a sum rate comparison.For a given search range L, the optimal brute-force search scheme maysearch over about

$\frac{L\left( {L + 1} \right)}{2}$possible user groups. The search associated with graph 602 may compriseabout 5,050 user groups. The search associated with the graph 604 maycomprise about 55 users groups. The reduced search may represent anearly 99% reduction in searched user groups in comparison to a fullsearch. The capacity performance associated with the reduced range L=5graph 606 may be approximately equal to the capacity performance of thefull search graph 602. The search associated with the graph 606 maycomprise about 15 users groups. The capacity performance associated withthe reduced range L=2 graph 608 may be less than the capacityperformance of the full search graph 602. The search associated with thegraph 608 may comprise about 3 users groups. Various embodiments of theinvention may reduce computational burden at the base station 200 incomparison to a conventional full search method, while achievingcomparable capacity performance.

FIG. 7 is a chart illustrating exemplary bit error rate (BER)performance associated with various downlink transmission schemes, inaccordance with an embodiment of the invention. The chart may be basedon exemplary conditions in which the total number of users K=100 and thenumber of transmitting antenna at the base station M=2. Each user 240may comprise a single receiving antenna 228. The channels for eachchannel realization may be generated to be flat Rayleigh faded channels.The transmit antennas 224 . . . 226 at the base station 200 may beplaced at a sufficient distance that individual channels may experiencefading that is independent of the fading in other channels. Themodulation format utilized may be QPSK.

Referring to FIG. 7, there is shown a graph illustrating the BERperformance of a downlink scheme in which the reduced range value L=100,702, a graph illustrating the BER performance of a downlink scheme inwhich the reduced range value L=10, 704, a graph illustrating the BERperformance of a downlink scheme in which the reduced range value L=5,706, and a graph illustrating the BER performance of a downlink schemein which the reduced range value L=2, 708. In the graph 702, the reducedrange may be equal to the full range, that is, the graph may illustratethe full search capacity performance.

In the graph 704, the reduced search may search a subset comprising onlythe 10 strongest users. The BER performance associated with the reducedrange L=10 graph 704 may be substantially equal to the BER performanceof the full search graph 702. The BER performance associated with thereduced range L=5 graph 706 may be approximately equal to the capacityperformance of the full search graph 702. The BER performance associatedwith the reduced range L=2 graph 708 may be less than the BERperformance of the full search graph 702. Various embodiments of theinvention may reduce computational burden at the base station 200 incomparison to a conventional full search method, while achievingcomparable BER performance.

Furthermore, in a frequency division duplex (FDD) system where the basestation 200 may obtain the CSI from the users 240 . . . 250 via afeedback link, the amount of feedback may also be reduced in variousembodiments of the invention, when compared to conventional full searchmethods, because the base station 200 may only need full CSI knowledgefrom the L strongest users instead of all the K users. Full CSIknowledge may comprise channel gain information and channel directioninformation. In various embodiments of the invention, the base station200 may need channel direction information from the L strongest users.For example, if a channel corresponding to each user may becharacterized by a channel estimate h_(κ), B_(g) bits may be utilized toquantize the channel gain ∥h_(κ)∥² and B_(v) bits may be utilized toquantize channel direction

$\frac{h_{\kappa}}{{h_{\kappa}}^{2}}.$In this case, the total amount of feedback, in various embodiments ofthe invention may be given by the following expression:B=K·B _(g) +L·B _(v).  (18)where the number of bits in, B_(v) may be much larger than the number ofbits in B_(g) in many systems. Therefore, one aspect of the invention,which comprises reducing the range L in comparison to the full range K,may reduce the amount of feedback information that is processed by thebase station 200. Table I gives a summary of the amount of feedback forvarious choices of L when B_(g)=2 bits and B_(v)=6 bits.

TABLE I Searching Range L 100 10 5 2 The Number of Possible User Groups5,050 55 15 3 Feedback Amount B (bits) 800 260 230 212

A system for communicating information in a communications system maycomprise a range reduction processor 206 that determines a plurality ofchannel measurements corresponding to a plurality of signals. The rangereduction processor 206 may compute a plurality of channel capacitiesbased on the channel measurements corresponding to a subset of theplurality of signals having channel gain that is greater than aremaining portion of the plurality signals. The range reductionprocessor 206 may determine the channel gain associated with each of theplurality of signals based on the plurality of channel measurements.Each of the plurality of signals may be ranked based on a correspondingchannel gain.

The range reduction processor 206 may select a subset of the pluralityof signals based on the ranking and compute a number of instances inwhich each of the plurality of signals is one of the subset. Acumulative distribution function, for example, may be computed based ona plurality of the number of instances corresponding to a rankedplurality of signals. The range reduction processor 206 may select athreshold value based on the cumulative distribution function. Thethreshold may determine a number of signals that are to be included inthe subset. An information transfer rate, or sum rate, associated witheach of the selection portion of the plurality of signals, may becomputed. At least one of the plurality of signals may correspond to atleast one of a plurality mobile terminals, or users 240.

Accordingly, the present invention may be realized in hardware,software, or a combination thereof. The present invention may berealized in a centralized fashion in at least one computer system, or ina distributed fashion where different elements may be spread acrossseveral interconnected computer systems. Any kind of computer system orother apparatus adapted for carrying out the methods described hereinmay be suited. A typical combination of hardware and software may be ageneral-purpose computer system with a computer program that, when beingloaded and executed, may control the computer system such that itcarries out the methods described herein.

The present invention may also be embedded in a computer programproduct, which comprises all the features enabling the implementation ofthe methods described herein, and which when loaded in a computer systemis able to carry out these methods. Computer program in the presentcontext means any expression, in any language, code or notation, of aset of instructions intended to cause a system having an informationprocessing capability to perform a particular function either directlyor after either or both of the following: a) conversion to anotherlanguage, code or notation; b) reproduction in a different materialform.

While the present invention has been described with reference to certainembodiments, it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted withoutdeparting from the scope of the present invention. In addition, manymodifications may be made to adapt a particular situation or material tothe teachings of the present invention without departing from its scope.Therefore, it is intended that the present invention not be limited tothe particular embodiment disclosed, but that the present invention willinclude all embodiments falling within the scope of the appended claims.

1. A method for communicating information in a communications system,the method comprising: computing a plurality of channel measurements,each corresponding to one or more of a plurality of signals; identifyingeach of a plurality of users based on said plurality of signals;selecting a subset of said plurality of users based on said computedplurality of channel measurements; computing a plurality of channelcapacities for said selected subset based on one or more of saidcomputed plurality of channel measurements wherein channel capacitiesare not computed for each of said plurality of users that is not withinsaid selected subset; selecting a group of users from said selectedsubset based on said computed plurality of channel capacities; andconcurrently transmitting signals to said selected group of users. 2.The method according to claim 1, comprising computing a plurality ofchannel gain values for said plurality of users based on said computedplurality of channel measurements.
 3. The method according to claim 2,comprising ranking said each of said plurality of users based on saidcomputed plurality of channel gain values.
 4. The method according toclaim 3, comprising selecting said subset based on said ranking.
 5. Themethod according to claim 1, comprising computing a number of instancesin which each of said plurality of users is one of said selected subset.6. The method according to claim 5, comprising computing a cumulativedistribution function based on said computed number of instances forsaid each of said plurality of users.
 7. The method according to claim6, comprising selecting a threshold value based on said computedcumulative distribution function.
 8. The method according to claim 7,comprising determining a number of users among said plurality of usersthat are selected for said subset based on said threshold.
 9. The methodaccording to claim 1, comprising computing an information transfer ratefor each user among said selected subset based on a correspondingportion of said computed plurality of channel capacities.
 10. The methodaccording to claim 1, wherein said each of said plurality of userscorresponds to a mobile terminal.
 11. A system for communicatinginformation in a communications system, the system comprising: one ormore processors that are operable to compute a plurality of channelmeasurements, each corresponding to one or more of a plurality ofsignals; said one or more processors are operable to identify each of aplurality of users based on said plurality of signals; said one or moreprocessors are operable to select a subset of said plurality of usersbased on said computed plurality of channel measurements; said one ormore processors are operable to compute a plurality of channelcapacities for said selected subset based on one or more of saidcomputed plurality of channel measurements wherein channel capacitiesare not computed for each of said plurality of users that is not withinsaid selected subset; said one or more processors are operable to selecta group of users from said selected subset based on said computedplurality of channel capacities; and said one or more processors areoperable to concurrently transmit signals to said selected group ofusers.
 12. The system according to claim 11, wherein said one or moreprocessors are operable to compute a plurality of channel gain valuesfor said plurality of users based on said computed plurality of channelmeasurements.
 13. The system according to claim 12, wherein said one ormore processors are operable to rank said each of said plurality ofusers based on said computed plurality of channel gain values.
 14. Thesystem according to claim 13, wherein said one or more processors areoperable to select said subset based on said ranking.
 15. The systemaccording to claim 11, wherein said one or more processors are operableto compute a number of instances in which each of said plurality ofusers is one of said selected subset.
 16. The system according to claim15, wherein said one or more processors are operable to compute acumulative distribution function based on said computed number ofinstances for said each of said plurality of users.
 17. The systemaccording to claim 16, wherein said one or more processors are operableto select a threshold value based on said computed cumulativedistribution function.
 18. The system according to claim 17, whereinsaid one or more processors are operable to determine a number of usersamong said plurality of users that are selected for said subset based onsaid threshold.
 19. The system according to claim 11, wherein said oneor more processors are operable to compute an information transfer ratefor each user among said selected subset based on a correspondingportion of said computed plurality of channel capacities.
 20. The systemaccording to claim 11, wherein said each of said plurality of userscorresponds to a mobile terminal.
 21. The system according to claim 11,wherein said one or more processors comprise a range reductionprocessor.